Description: Interpretable and Annotation-Efficient Learning for Medical Image Computing Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4?8, 2020, Proceedings Author(s): Jaime Cardoso, Hien Van Nguyen, Nicholas Heller, Pedro Henriques Abreu, Ivana Isgum, Wilson Silva, Ricardo Cruz, Jose Pereira Amorim, Vishal Patel, Badri Roysam Format: Paperback Publisher: Springer Nature Switzerland AG, Switzerland Imprint: Springer Nature Switzerland AG ISBN-13: 9783030611651, 978-3030611651 Synopsis This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.
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Book Title: Interpretable and Annotation-Efficient Learning for Medical Im...
Item Height: 235 mm
Item Width: 155 mm
Series: Image Processing, Computer Vision, Pattern Recognition, and Graphics
Author: Jaime Cardoso, Nicholas Heller, Ricardo Cruz, Pedro Henriques Abreu, Ivana Isgum, Hien Van Nguyen, Wilson Silva, Vishal Patel, Badri Roysam, Jose Pereira Amorim
Publication Name: Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, MIL3ID 2020, and 5th International Workshop, LABELS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings
Format: Paperback
Language: English
Publisher: Springer Nature Switzerland A&G
Subject: Medicine, Engineering & Technology, Computer Science
Publication Year: 2020
Type: Textbook
Item Weight: 486 g
Number of Pages: 292 Pages
